Steerable-Scalable Kernels for Edge Detection and Junction Analysis

Families of kernels that are useful in a variety of early vision algorithms may be obtained by rotating and scaling in a continuum a `template' kernel. These multi-scale multi-orientation family may be approximated by linear interpolation of a discrete nite set of appropriatèbasis' kernels. A scheme for generating such a basis together with the appropriate interpolation weights is described. Unlike previous schemes by Perona, and Simoncelli et al. it is guaranteed to generate the most parsimonious one. Additionally, it is shown how to exploit two symmetries in edge-detection kernels for reducing storage and computational costs and generating simultaneously endstop-and junction-tuned lters for free.